processor

README

General Information

Processors are used at various stages of a pipeline. Generally, a processor pre-processes data before it is exported (e.g. modify attributes or sample) or helps ensure that data makes it through a pipeline successfully (e.g. batch/retry).

Some important aspects of pipelines and processors to be aware of:

• Recommended Processors
• Data Ownership
• Exclusive Ownership
• Shared Ownership
• Ordering Processors

Supported processors (sorted alphabetically):

• Attributes Processor
• Batch Processor
• Filter Processor
• Memory Limiter Processor
• Resource Processor
• Probabilistic Sampling Processor
• Span Processor

The contributors repository has more processors that can be added to custom builds of the Collector.

Recommended Processors

By default, no processors are enabled. Depending on the data source, it may be recommended that multiple processors be enabled. Processors must be enabled for every data source: Not all processors support all data sources. In addition, it is important to note that the order of processors matters. The order in each section below is the best practice. Refer to the individual processor documentation for more information.

Traces

1. memory_limiter
2. any sampling processors
3. batch
4. any other processors

Metrics

1. memory_limiter
2. batch
3. any other processors

Data Ownership

The ownership of the TraceData and MetricsData in a pipeline is passed as the data travels through the pipeline. The data is created by the receiver and then the ownership is passed to the first processor when ConsumeTraceData/ConsumeMetricsData function is called.

Note: the receiver may be attached to multiple pipelines, in which case the same data will be passed to all attached pipelines via a data fan-out connector.

From data ownership perspective pipelines can work in 2 modes:

• Exclusive data ownership
• Shared data ownership

The mode is defined during startup based on data modification intent reported by the processors. The intent is reported by each processor via MutatesConsumedData field of the struct returned by GetCapabilities function. If any processor in the pipeline declares an intent to modify the data then that pipeline will work in exclusive ownership mode. In addition, any other pipeline that receives data from a receiver that is attached to a pipeline with exclusive ownership mode will be also operating in exclusive ownership mode.

Exclusive Ownership

In exclusive ownership mode the data is owned exclusively by a particular processor at a given moment of time and the processor is free to modify the data it owns.

Exclusive ownership mode is only applicable for pipelines that receive data from the same receiver. If a pipeline is marked to be in exclusive ownership mode then any data received from a shared receiver will be cloned at the fan-out connector before passing further to each pipeline. This ensures that each pipeline has its own exclusive copy of data and the data can be safely modified in the pipeline.

The exclusive ownership of data allows processors to freely modify the data while they own it (e.g. see attributesprocessor). The duration of ownership of the data by processor is from the beginning of ConsumeTraceData/ConsumeMetricsData call until the processor calls the next processor's ConsumeTraceData/ConsumeMetricsData function, which passes the ownership to the next processor. After that the processor must no longer read or write the data since it may be concurrently modified by the new owner.

Exclusive Ownership mode allows to easily implement processors that need to modify the data by simply declaring such intent.

Shared Ownership

In shared ownership mode no particular processor owns the data and no processor is allowed the modify the shared data.

In this mode no cloning is performed at the fan-out connector of receivers that are attached to multiple pipelines. In this case all such pipelines will see the same single shared copy of the data. Processors in pipelines operating in shared ownership mode are prohibited from modifying the original data that they receive via ConsumeTraceData/ConsumeMetricsData call. Processors may only read the data but must not modify the data.

If the processor needs to modify the data while performing the processing but does not want to incur the cost of data cloning that Exclusive mode brings then the processor can declare that it does not modify the data and use any different technique that ensures original data is not modified. For example, the processor can implement copy-on-write approach for individual sub-parts of TraceData/MetricsData argument. Any approach that does not mutate the original TraceData/MetricsData argument (including referenced data, such as Node, Resource, Spans, etc) is allowed.

If the processor uses such technique it should declare that it does not intend to modify the original data by setting MutatesConsumedData=false in its capabilities to avoid marking the pipeline for Exclusive ownership and to avoid the cost of data cloning described in Exclusive Ownership section.

Ordering Processors

The order processors are specified in a pipeline is important as this is the order in which each processor is applied to traces and metrics.

Include/Exclude Metrics

The filter processor exposes the option to provide a set of metric names to match against to determine if the metric should be included or excluded from the processor. To configure this option, under include and/or exclude both match_type and metrics_names are required.

Note: If both include and exclude are specified, the include properties are checked before the exclude properties.

filter:
  # metrics indicates this processor applies to metrics
  metrics:
    # include and/or exclude can be specified. However, the include properties
    # are always checked before the exclude properties.
    {include, exclude}:
      # match_type controls how items matching is done.
      # Possible values are "regexp" or "strict".
      # This is a required field.
      match_type: {strict, regexp}

      # regexp is an optional configuration section for match_type regexp.
      regexp:
        # < see "Match Configuration" below >

      # metric_names specify an array of items to match the metric name against.
      # This is a required field.
      metric_names: [<item1>, ..., <itemN>]

Match Configuration

Some match_type values have additional configuration options that can be specified. The match_type value is the name of the configuration section. These sections are optional.

# regexp is an optional configuration section for match_type regexp.
regexp:
  # cacheenabled determines whether match results are LRU cached to make subsequent matches faster.
  # Cache size is unlimited unless cachemaxnumentries is also specified.
  cacheenabled: <bool>
  # cachemaxnumentries is the max number of entries of the LRU cache; ignored if cacheenabled is false.
  cachemaxnumentries: <int>

Include/Exclude Spans

The attribute processor and the span processor expose the option to provide a set of properties of a span to match against to determine if the span should be included or excluded from the processor. To configure this option, under include and/or exclude at least match_type and one of services, span_names or attributes is required.

Note: If both include and exclude are specified, the include properties are checked before the exclude properties.

{span, attributes}:
    # include and/or exclude can be specified. However, the include properties
    # are always checked before the exclude properties.
    {include, exclude}:
      # At least one of services, span_names or attributes must be specified.
      # It is supported to have more than one specified, but all of the specified
      # conditions must evaluate to true for a match to occur.

      # match_type controls how items in "services" and "span_names" arrays are
      # interpreted. Possible values are "regexp" or "strict".
      # This is a required field.
      match_type: {strict, regexp}

      # regexp is an optional configuration section for match_type regexp.
      regexp:
        # < see "Match Configuration" below >

      # services specify an array of items to match the service name against.
      # A match occurs if the span service name matches at least of the items.
      # This is an optional field.
      services: [<item1>, ..., <itemN>]

      # The span name must match at least one of the items.
      # This is an optional field.
      span_names: [<item1>, ..., <itemN>]

      # Attributes specifies the list of attributes to match against.
      # All of these attributes must match exactly for a match to occur.
      # This is an optional field.
      attributes:
          # Key specifies the attribute to match against.
        - key: <key>
          # Value specifies the exact value to match against.
          # If not specified, a match occurs if the key is present in the attributes.
          value: {value}

Match Configuration

Some match_type values have additional configuration options that can be specified. The match_type value is the name of the configuration section. These sections are optional.

# regexp is an optional configuration section for match_type regexp.
regexp:
  # cacheenabled determines whether match results are LRU cached to make subsequent matches faster.
  # Cache size is unlimited unless cachemaxnumentries is also specified.
  cacheenabled: <bool>
  # cachemaxnumentries is the max number of entries of the LRU cache; ignored if cacheenabled is false.
  cachemaxnumentries: <int>

Documentation

Index

• Variables
• func MetricTagKeys() []tag.Key
• func MetricViews() []*view.View
• func NewLogsCloningFanOutConnector(lcs []consumer.LogsConsumer) consumer.LogsConsumer
• func NewLogsFanOutConnector(lcs []consumer.LogsConsumer) consumer.LogsConsumer
• func NewMetricsCloningFanOutConnector(mcs []consumer.MetricsConsumer) consumer.MetricsConsumer
• func NewMetricsFanOutConnector(mcs []consumer.MetricsConsumer) consumer.MetricsConsumer
• func NewTracesCloningFanOutConnector(tcs []consumer.TracesConsumer) consumer.TracesConsumer
• func NewTracesFanOutConnector(tcs []consumer.TracesConsumer) consumer.TracesConsumer
• func RecordsSpanCountMetrics(ctx context.Context, scm *SpanCountStats, measure *stats.Int64Measure)
• type SpanCountStats
  • func NewSpanCountStats(td pdata.Traces) *SpanCountStats
  • func (scm *SpanCountStats) GetAllSpansCount() int

Variables

var (
	TagServiceNameKey, _   = tag.NewKey("service")
	TagProcessorNameKey, _ = tag.NewKey(obsreport.ProcessorKey)

	StatReceivedSpanCount = stats.Int64(
		"spans_received",
		"counts the number of spans received",
		stats.UnitDimensionless)
	StatDroppedSpanCount = stats.Int64(
		"spans_dropped",
		"counts the number of spans dropped",
		stats.UnitDimensionless)

	StatTraceBatchesDroppedCount = stats.Int64(
		"trace_batches_dropped",
		"counts the number of trace batches dropped",
		stats.UnitDimensionless)
)

Keys and stats for telemetry.

Functions

func MetricTagKeys

func MetricTagKeys() []tag.Key

MetricTagKeys returns the metric tag keys according to the given telemetry level.

func MetricViews

func MetricViews() []*view.View

MetricViews return the metrics views according to given telemetry level.

func NewLogsCloningFanOutConnector

func NewLogsCloningFanOutConnector(lcs []consumer.LogsConsumer) consumer.LogsConsumer

NewLogsCloningFanOutConnector wraps multiple trace consumers in a single one.

func NewLogsFanOutConnector

func NewLogsFanOutConnector(lcs []consumer.LogsConsumer) consumer.LogsConsumer

NewLogsFanOutConnector wraps multiple log consumers in a single one.

func NewMetricsCloningFanOutConnector

func NewMetricsCloningFanOutConnector(mcs []consumer.MetricsConsumer) consumer.MetricsConsumer

NewMetricsCloningFanOutConnector wraps multiple metrics consumers in a single one and clones the data before fanning out.

func NewMetricsFanOutConnector

func NewMetricsFanOutConnector(mcs []consumer.MetricsConsumer) consumer.MetricsConsumer

NewMetricsFanOutConnector wraps multiple metrics consumers in a single one.

func NewTracesCloningFanOutConnector

func NewTracesCloningFanOutConnector(tcs []consumer.TracesConsumer) consumer.TracesConsumer

NewTracesCloningFanOutConnector wraps multiple traces consumers in a single one and clones the data before fanning out.

func NewTracesFanOutConnector

func NewTracesFanOutConnector(tcs []consumer.TracesConsumer) consumer.TracesConsumer

NewTracesFanOutConnector wraps multiple trace consumers in a single one.

func RecordsSpanCountMetrics

func RecordsSpanCountMetrics(ctx context.Context, scm *SpanCountStats, measure *stats.Int64Measure)

RecordsSpanCountMetrics reports span count metrics for specified measure.

Types

type SpanCountStats

type SpanCountStats struct {
	// contains filtered or unexported fields
}

SpanCountStats represents span count stats grouped by service if DETAILED telemetry level is set, otherwise only overall span count is stored in serviceSpansCounts.

func NewSpanCountStats

func NewSpanCountStats(td pdata.Traces) *SpanCountStats

func (*SpanCountStats) GetAllSpansCount

func (scm *SpanCountStats) GetAllSpansCount() int